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Evolutionary Computation to Estimate Volatility

EasyChair Preprint no. 301

7 pagesDate: June 24, 2018

Abstract

A performance evaluation study is implemented among the methods of Genetic Algorithms with Floating Point representation and some traditional optimization methods in the task of estimating the parameters of a GARCH (1,1) Normal process using artificial data obtained by simulation. The results show that the approximate solutions obtained by means of Genetic Algorithms present a better stability and precision with respect to the traditional optimization methods. The choice of the initial point in the numerical optimization methods is not a critical condition in the use of the Genetic Algorithms as a method to find the solution. Finally, the use of the method of Genetic Algorithms in the finding of the solution of the vector of parameters of the likelihood function of a model GARCH (1,1) t-Student for data of rates of exchange returns of the Sol versus the Dollar.

Keyphrases: Algoritmos Genéticos, GARCH, Inferencia estadística

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:301,
  author = {Luis A. Navarro Huamaní},
  title = {Evolutionary Computation to Estimate Volatility},
  howpublished = {EasyChair Preprint no. 301},

  year = {EasyChair, 2018}}
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